NayePankh Foundation — Data Analytics Project

Focus Areas: Food Distribution & Volunteer Engagement

About NayePankh Foundation

NayePankh ("new wings") is a youth-led Indian NGO that started during the COVID-19 pandemic and has since expanded to provide food, sanitary napkins, clothes, and education support to underprivileged communities — having helped over 2 lakh people to date.

Project Overview

This project demonstrates how data analytics can help NayePankh track impact, identify gaps, and make data-driven decisions. It analyzes a synthetic sample dataset (12 months) covering:

• Food Distribution — packets distributed by region (Bengaluru, Mysuru, Hubli, Mangaluru, Belagavi) and by beneficiary type (Families, Children, Elderly, Stray Animals)
• Volunteer Engagement — volunteer participation by category (College Students, School Students, Working Professionals, Others) and by activity type (Food Distribution, Sanitary Kit Awareness, Clothes Donation Drive, Education Support)

Each dimension is visualized using pie charts with percentage breakdowns, followed by key insights and recommendations for the foundation.

Repository Structure

nayepankh-data-analytics/
├── README.md
├── dashboard.html                          # Interactive dashboard (open in any browser)
├── analysis.ipynb                          # Main analysis notebook (with outputs)
├── generate_data.py                        # Script to generate the sample dataset
├── generate_charts.py                      # Script to generate pie charts
├── NayePankh_Data_Analytics_Project.xlsx   # Excel workbook (data + formulas + charts)
├── data/
│   ├── food_distribution.csv
│   ├── volunteer_engagement.csv
│   └── volunteer_by_activity.csv
└── visualizations/
    ├── food_by_region.png
    ├── food_by_beneficiary_type.png
    ├── volunteers_by_category.png
    └── volunteers_by_activity.png

Interactive Dashboard

dashboard.html is directly integrated with NayePankh_Data_Analytics_Project.xlsx — when opened over a server (e.g. GitHub Pages, or a local web server), it automatically reads and parses the Excel workbook in the browser (using SheetJS) and builds all four pie charts from its raw data. Edit the data in the Excel file, refresh the dashboard, and the charts update — no manual edits to the dashboard code needed.

To get a shareable live link:

1. Push this repo to GitHub (keep dashboard.html and NayePankh_Data_Analytics_Project.xlsx in the same folder).
2. Go to Settings → Pages.
3. Under "Build and deployment", set Source to "Deploy from a branch", choose the main branch and / (root) folder, then save.
4. GitHub will publish a link like https://<username>.github.io/nayepankh-data-analytics/dashboard.html.

To run it locally (so it can fetch the Excel file — opening the HTML file directly via double-click won't work due to browser security restrictions on local file access):

python -m http.server 8000

then open http://localhost:8000/dashboard.html in your browser.

If opened via file:// (double-clicked) or if the Excel file can't be read, the dashboard falls back to a bundled snapshot of the same data, so it still displays correctly — just without the live link to the spreadsheet.

The dashboard shows all four pie charts (Food Distribution by Region & Beneficiary Type, Volunteer Engagement by Category & Activity Type), each with hover tooltips and a breakdown list showing raw counts (value / total) alongside the percentage share.

How to Run

pip install pandas matplotlib openpyxl jupyter
jupyter notebook analysis.ipynb

Key Insights

• Food Distribution: Bengaluru leads in food packets distributed; Families receive the largest share, followed by Children, Elderly, and Stray Animals.
• Volunteer Engagement: College and school students together make up over 75% of volunteers — reflecting NayePankh's youth-led identity. Food distribution drives attract the highest volunteer participation.

Recommendations

1. Expand food distribution outreach in under-served regions (e.g., Belagavi, Hubli).
2. Leverage the strong student volunteer base to mentor and onboard more diverse volunteers.
3. Track region/category-level data consistently each month to monitor trends over time.
4. Use visual impact breakdowns to strengthen fundraising and donor transparency.

Note on Data

The dataset used in this project is synthetically generated for demonstration purposes, as real operational data from NayePankh Foundation was not available. The structure mirrors the kind of data the foundation could realistically track.

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This project was created as part of a Data Analytics Internship assignment.